In the age of convergence, customer churn is a concern for service providers, challenging most retention techniques...
By definition Prediction refers to the possibility of an event happening. The possibility in most cases is measured in terms of percentage like the possibility of a customer churning is 80%. That means that there is a 20% chance that the customer might not churn.
There have been a lot of talks about using predictive analytics across all aspects of the world starting right from healthcare to sports and a lot has been spoken about how it is going to revolutionise the world by providing actionable insights.
I think the key over here is the word INSIGHT and if one looks at this more closely this refers more to information in a particular context. If one understands the significance of this nuance correctly the gains from using predictive analytics can be increased multi-fold.
There are two important aspects that one should take note of while deciding to use predictive analytics or rather before investing in a predictive analytics project.
- Will it really help?
- Can the results be used by the business correctly?
One needs to realise that predictive analytics contrary to claims made by so many technology companies cannot be used everywhere. Like for example most predictions associated with sports go wrong especially when we are trying to identify a winner, that being said we can use predictive analytics to enhance the performance of a team.
The second part is more important can the results be used by the business to increase impact. A typical example would be customer churn. Many products and solutions do a very good job at predicting customer churn but the most important part is why is a customer going to churn and how can it be stopped.
While conceiving a predictive analytics project it is important that one must visualise the end result in a manner in which it brings large value to the business and helps in interpreting results correctly, which in my opinion is the most important part.